World's Best Scientists 2026 revealed!
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Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
155
Citations
114652
World Ranking
27
National Ranking
16

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2018 - IEEE Fellow For contributions to apprenticeship and reinforcement learning for robotics and autonomous systems
  • 2011 - Hellman Fellow
  • 2011 - Fellow of Alfred P. Sloan Foundation

Overview

Pieter Abbeel is affiliated with the University of California, Berkeley in the United States. Their research primarily focuses on computer science, with extensive contributions to subfields including artificial intelligence, computer vision and pattern recognition, control and systems engineering, biomedical engineering, and electrical and electronic engineering.

Their work spans multiple main topics such as reinforcement learning in robotics, multimodal machine learning applications, domain adaptation and few-shot learning, robot manipulation and learning, human pose and action recognition, topic modeling, and adversarial robustness in machine learning.

Abbeel has authored numerous papers, including recent publications such as:

  • "Denoising Diffusion Probabilistic Models," 2020, arXiv (Cornell University)
  • "Decision Transformer: Reinforcement Learning via Sequence Modeling," 2021, arXiv (Cornell University)
  • "CURL: Contrastive Unsupervised Representations for Reinforcement Learning," 2020, arXiv (Cornell University)
  • "Zero-Shot Text-Guided Object Generation with Dream Fields," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "AMP," 2021, ACM Transactions on Graphics

Frequent coauthors collaborating with Abbeel include:

  • Kimin Lee
  • Sergey Levine
  • Lerrel Pinto
  • Stephen James
  • Younggyo Seo

The primary venues for Abbeel's publications are:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • ACM Transactions on Graphics

Abbeel has contributed to book publications, including one with Springer International Publishing titled "Algorithmic Foundations of Robotics XII," released in 2020.

The scientist has received several awards, such as being named an IEEE Fellow in 2018 for contributions to apprenticeship and reinforcement learning for robotics and autonomous systems. They were also recognized as a Hellman Fellow in 2011 and as a Fellow of the Alfred P. Sloan Foundation in 2011.

Best Publications

  • Model-agnostic meta-learning for fast adaptation of deep networks

    Chelsea Finn;Pieter Abbeel;Sergey Levine

  • Denoising Diffusion Probabilistic Models.

    Jonathan Ho;Ajay Jain;Pieter Abbeel

  • Trust Region Policy Optimization

    John Schulman;Sergey Levine;Pieter Abbeel;Michael Jordan

  • Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

    Tuomas Haarnoja;Aurick Zhou;Pieter Abbeel;Sergey Levine

  • InfoGAN: interpretable representation learning by information maximizing generative adversarial nets

    Xi Chen;Yan Duan;Rein Houthooft;John Schulman

  • Apprenticeship learning via inverse reinforcement learning

    Pieter Abbeel;Andrew Y. Ng

  • End-to-end training of deep visuomotor policies

    Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel

  • Domain randomization for transferring deep neural networks from simulation to the real world

    Josh Tobin;Rachel Fong;Alex Ray;Jonas Schneider

  • Trust Region Policy Optimization

    John Schulman;Sergey Levine;Philipp Moritz;Michael I. Jordan

  • High-Dimensional Continuous Control Using Generalized Advantage Estimation

    John Schulman;Philipp Moritz;Sergey Levine;Michael Jordan

  • Soft Actor-Critic Algorithms and Applications

    Tuomas Haarnoja;Aurick Zhou;Kristian Hartikainen;George Tucker

  • Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

    Xue Bin Peng;Marcin Andrychowicz;Wojciech Zaremba;Pieter Abbeel

  • Benchmarking deep reinforcement learning for continuous control

    Yan Duan;Xi Chen;Rein Houthooft;John Schulman

  • Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

    Ryan Lowe;Yi Wu;Aviv Tamar;Jean Harb

  • Bottleneck Transformers for Visual Recognition

    Aravind Srinivas;Tsung-Yi Lin;Niki Parmar;Jonathon Shlens

  • A Survey of Research on Cloud Robotics and Automation

    Ben Kehoe;Sachin Patil;Pieter Abbeel;Ken Goldberg

  • A Simple Neural Attentive Meta-Learner

    Nikhil Mishra;Mostafa Rohaninejad;Xi Chen;Pieter Abbeel

  • Reinforcement learning with deep energy-based policies

    Tuomas Haarnoja;Haoran Tang;Pieter Abbeel;Sergey Levine

  • DeepMimic: example-guided deep reinforcement learning of physics-based character skills

    Xue Bin Peng;Pieter Abbeel;Sergey Levine;Michiel van de Panne

  • An Application of Reinforcement Learning to Aerobatic Helicopter Flight

    Pieter Abbeel;Adam Coates;Morgan Quigley;Andrew Y. Ng

  • Guided cost learning: deep inverse optimal control via policy optimization

    Chelsea Finn;Sergey Levine;Pieter Abbeel

  • RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning

    Yan Duan;John Schulman;Xi Chen;Peter L. Bartlett

Frequent Co-Authors

Sergey Levine
Sergey Levine University of California, Berkeley
Hugh Durrant-Whyte
Hugh Durrant-Whyte University of Sydney
Ken Goldberg
Ken Goldberg University of California, Berkeley
Chelsea Finn
Chelsea Finn Stanford University
Sachin Patil
Sachin Patil University of California, Berkeley
Aviv Tamar
Aviv Tamar Technion – Israel Institute of Technology
Andrew Y. Ng
Andrew Y. Ng Stanford University
Trevor Darrell
Trevor Darrell University of California, Berkeley

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